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The Programming Podcast

The Programming Podcast

Leon Noel and Danny Thompson explain technical problems, industry information, career advice and more on The Programming Podcast!

Danny Thompson, Director of Technology @ This Dot Labs
Leon Noel, Managing Director @ Resilient Coders & 100Devs

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  • 24 episodes
  • fortnightly
  • Avg 52 min
  • English

Episodes 24

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  • January 27 · 41 min

    This Apple Decision Proves Gemini is King! (ft Logan Kilpatrick from the Google DeepMind team)

    Apple’s latest decision to team with Google and use Gemini signals something bigger than a headline. It highlights how far Gemini has come, and why utility is becoming the real battleground for AI in 2026. In this episode, we sit down with Logan Kilpatrick from the Gemini team at Google DeepMind to talk about what this moment means for developers, builders, and teams choosing tools today. We get into the evolution of Gemini, why multimodality and long context mattered early, and how Gemini is increasingly showing up where people already work through products like Workspace, Gmail, and Docs. ✅ If you made it to the end, comment: “Documentation Traffic.” so we know who is a real one! SITE https://www.programmingpodcast.com/ Stay in Touch: 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Danny Thompson https://x.com/DThompsonDev / dthompsondev www.DThompsonDev.com Leon Noel https://x.com/leonnoel / leonnoel https://100devs.org/ 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Subscribe if you want honest, no-hype conversations about where software development is heading in 2026. We also dig into what’s next for image generation and editing, including the leap in text accuracy, practical features creators want (like layers and transparent backgrounds), and the role of technologies like SynthID in building trust as AI content becomes more common. Plus: vibe coding, reasoning models, prompt expansion, how developers can ask for more ambitious outcomes, and why proactivity and proof-of-work experiences are shaping the next wave of AI products. If you’re building with AI or trying to stay ahead as a developer, this episode will give you a clear view into where things are going. 0:00 Apple’s decision changes the AI narrative 1:11 Meet today’s guest: Logan Kilpatrick (Gemini team) 1:52 Apple quote and why this is big for Gemini 2:19 Are the model wars over, and are utility wars next? 4:20 Gemini in real workflows: Workspace, Gmail, and daily utility 5:26 AI hype vs reality: why products must meet users where they are 6:42 Balancing frontier progress with “do not break the product” responsibility 8:25 Nano Banana and the leap in text accurate images 9:28 Infographics, presentations, and where image generation is going 10:00 Why text rendering became a key metric for model quality 12:19 What users want next: transparent backgrounds, layers, better editing 13:52 Trust and safety: SynthID, watermarking, and standardization challenges 16:06 What developers ask for most: quota and scale 17:38 Logs and the idea of an agent that finds missing product features 19:31 How people should use AI differently: native form factors, proactivity 21:19 Proof of work and why Deep Research builds trust fast 24:53 Prompt expansion, reasoning models, and why “prompt wizardry” is fading 26:40 Ask for more: why ambition is now the best prompting strategy 29:36 Frameworks vs over optimizing prompts: when it matters, when it does not 32:52 What “vibe coding” means now and how it is evolving 34:08 Teaching vibe coding on day one of school 36:01 Ask Danny, Leon, and Logan: feeling behind in tech 39:02 The moving finish line, authenticity, and turning the gap into a roadmap 41:25 Closing

  • January 20 · 51 min

    The death of Tailwind? What's happening to open source in 2026

    Is Tailwind actually “dying” or are we watching something bigger happen in real time? In this episode of The Programming Podcast, we dig into a brutal 2026 reality check for developers: the tools and communities we’ve relied on for years are getting shaken up by AI in ways most people didn’t see coming. We break down the Tailwind situation, including the GitHub discussion that surfaced a behind-the-curtain truth about modern open source: documentation traffic is the funnel, and AI is bypassing it. When LLMs stop sending humans to your docs, what happens to paid products, sponsorships, and ultimately the ability to maintain the framework at all? Then we shift to the other major signal: Stack Overflow’s decline. The charts are ugly, and the reasons are deeper than just “people use AI now.” We talk about the human cost of hostile community dynamics, why developer Q&A moved to Discord long before LLMs took off, and what it means when the internet’s most “verified” coding resource starts fading. Finally, we go big-picture: if open source loses funding and loses maintainers, what happens to the future of software? And where do new developers go when both the old learning paths and the new AI-heavy paths are unreliable? ✅ If you made it to the end, comment: “Documentation Traffic.” so we know who is a real one! SITE https://www.programmingpodcast.com/ Stay in Touch: 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Danny Thompson https://x.com/DThompsonDev / dthompsondev www.DThompsonDev.com Leon Noel https://x.com/leonnoel / leonnoel https://100devs.org/ 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Subscribe if you want honest, no-hype conversations about where software development is heading in 2026.

  • January 8 · 1 hr 8 min

    The 2026 Coding Roadmap That Gets Results (Forget Everything Else!)

    It’s 2026. If you want to learn how to code and actually get results, this episode is your roadmap. Danny and Leon break down the stuff that is not “cool advice,” but is the advice that gets people hired. We talk about why course-hoarding feels productive but is not, what the real bottleneck in tech actually is (hint: not syntax), and how to build a learning system that survives the trough of sorrow. You’ll walk away with: The 3-part foundation that decides if you stick with coding Why “problem solving” is the real job, not typing code When AI helps, and when it quietly ruins your learning The learning science that separates dabblers from finishers (active recall + spaced repetition) How to pick what to learn based on your local market (and stop wasting years) Why community is the cheat code that keeps you moving when motivation dies If your goal is to learn to code this year, do one action right now: join a community, pick a path, and start. ✅ If you made it to the end, comment: “In 2026 I’m gonna grow my skill set.” SITE https://www.programmingpodcast.com/ Stay in Touch: 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Danny Thompson https://x.com/DThompsonDev / dthompsondev www.DThompsonDev.com Leon Noel https://x.com/leonnoel / leonnoel https://100devs.org/ 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Chapters 00:00 - The 2026 promise: real results, not cool advice 01:56 - Why you should listen to us (and why this episode goes viral) 04:18 - Start with your “why” or you will quit 05:45 - The 3 foundations: manage frustration, consistency, take care of yourself 09:11 - What companies pay for: problem solving, not syntax 12:30 - The shiny becomes dull: how people quit when novelty fades 15:49 - Build to derisk yourself: projects that help vs projects that hurt 17:15 - The trough of sorrow and crash of ineptitude 18:51 - AI as the easy exit, and why you cannot take it 21:09 - Hot take: earn AI later, do not use it early 22:46 - Over-reliance makes you weaker (and why teams sometimes remove AI) 25:36 - Learn how to learn: Barbara Oakley and rebuilding your foundation 26:45 - Active recall: do less work, remember more 27:45 - Spaced repetition: stop forgetting what you learn 30:11 - Build projects early to create incentive and momentum 32:25 - Make concepts “sticky” with analogies and mental models 33:31 - Your first year on the job decides career vs job 35:37 - Stop asking for “the tech stack” and start reading your local market 38:40 - Dallas example: 400 MERN jobs vs 34,000 Java jobs 42:31 - No one is taking risks right now: prove you can hit the ground running 44:16 - Target companies strategically (emerging talent programs, hiring volume) 47:00 - Do not marry a stack, marry adaptability 48:49 - System rules: make every project your own, break goals into daily actions 51:38 - Pick one path and finish: FreeCodeCamp, Odin Project, 100Devs, Full Stack Open 52:42 - The real cheat code: community 56:31 - Motivation expires, community keeps you moving 59:44 - Talent is distributed, opportunity is not 1:03:28 - Take action right now: join a community 1:04:34 - Comment to prove you are a real one 1:04:56 - “I don’t have time” and the priority truth 1:06:10 - Your value in 2026: caring, context, judgment, and composing solutions

  • Dec 30, 2025 · 59 min

    Why Even Staff Engineers Feel Behind on AI Right Now! (You are not alone!)

    The “prompt-and-pray” era is over — and that’s a good thing. In this episode, we break down why AI “magic” collapses under real production traffic (edge cases, hallucinations, messy inputs, and even infrastructure-level failures)… and what replaces it: actual AI engineering. Danny frames the shift with four architectural pillars that make LLM features shippable and reliable: - State orchestration (stop treating models like employees — they’re stateless CPUs) - Constraint generation (JSON forcing, schema-driven outputs, type-safe sampling) - Infrastructure reliability (retries, backoff, fallbacks — because inference can and will fail) - Regression testing & evals (measure prompts like code, break builds when quality drops) SITE https://www.programmingpodcast.com/ Stay in Touch: 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! Danny Thompson https://x.com/DThompsonDev / dthompsondev www.DThompsonDev.com Leon Noel https://x.com/leonnoel / leonnoel https://100devs.org/ 📧 Have questions for the show? Or are you a business that wants to talk business? Email us at dannyandleonspodcast@gmail.com! We also hit the reality of agent “throughput” vs human review bottlenecks (Phoenix Project vibes), why monolithic agents are a trap, and a listener question about networking + credibility after pitching an MVP that isn’t fully shipped yet. If you’re building AI features for real users — not demos — this is the blueprint. 00:00 — The “prompt-and-pray” era is over 02:49 — AI hype fades: guardrails + reality 06:34 — Deterministic software vs probabilistic models 07:29 — The 4 pillars of AI engineering (overview) 11:37 — Pillar 1: state orchestration (FSM, stateless models) 20:26 — Pillar 2: constraint generation (JSON, schemas, type safety) 28:28 — Pillar 3: infra reliability (retries, fallbacks, failures) 32:21 — Pillar 4: evals + regression testing (LLM-as-judge) 43:40 — Listener question: networking, MVP pressure, and credibility

Showing 21–24 of 24 episodes